© 2016 Contrast pattern-based classifiers are an important family of both understandable and accurate classifiers. Nevertheless, these classifiers do not achieve good performance on class imbalance problems. In this paper, we introduce a new contrast pattern-based classifier for class imbalance problems. Our proposal for solving the class imbalance problem combines the support of the patterns with the class imbalance level at the classification stage of the classifier. From our experimental results, using highly imbalanced databases, we can conclude that our proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems. Additionally, we show that our classifier significantly outperforms other state-of-the-art classifiers not directly based on contrast patterns, which are also designed to deal with class imbalance problems.
Loyola-González, O., Medina-Pérez, M. A., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., Monroy, R., & García-Borroto, M. (2017). PBC4cip: A new contrast pattern-based classifier for class imbalance problems. Knowledge-Based Systems, 100-109. https://doi.org/10.1016/j.knosys.2016.10.018